MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis
<b>Background/Objectives</b>: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets an...
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2025-01-01
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| Series: | Metabolites |
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| author | Zheyuan Ou Xi Fu Dan Norbäck Ruqin Lin Jikai Wen Yu Sun |
| author_facet | Zheyuan Ou Xi Fu Dan Norbäck Ruqin Lin Jikai Wen Yu Sun |
| author_sort | Zheyuan Ou |
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| description | <b>Background/Objectives</b>: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. <b>Method</b>: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. <b>Results</b>: Our results revealed biologically meaningful biomarkers, such as <i>Bacteroides vulgatus</i> and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. <b>Conclusions</b>: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research. |
| format | Article |
| id | doaj-art-b278880169444a4ab65e608e33beab14 |
| institution | Kabale University |
| issn | 2218-1989 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Metabolites |
| spelling | doaj-art-b278880169444a4ab65e608e33beab142025-01-24T13:41:18ZengMDPI AGMetabolites2218-19892025-01-011515110.3390/metabo15010051MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic AnalysisZheyuan Ou0Xi Fu1Dan Norbäck2Ruqin Lin3Jikai Wen4Yu Sun5Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, ChinaOccupational and Environmental Medicine, Department of Medical Science, University Hospital, Uppsala University, 75237 Uppsala, SwedenGuangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, China<b>Background/Objectives</b>: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. <b>Method</b>: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. <b>Results</b>: Our results revealed biologically meaningful biomarkers, such as <i>Bacteroides vulgatus</i> and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. <b>Conclusions</b>: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.https://www.mdpi.com/2218-1989/15/1/51biomarker identificationlatent factordimension reductionfunctional pathway analysismulti-omics analysis |
| spellingShingle | Zheyuan Ou Xi Fu Dan Norbäck Ruqin Lin Jikai Wen Yu Sun MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis Metabolites biomarker identification latent factor dimension reduction functional pathway analysis multi-omics analysis |
| title | MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis |
| title_full | MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis |
| title_fullStr | MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis |
| title_full_unstemmed | MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis |
| title_short | MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis |
| title_sort | mimejf application of coupled matrix and tensor factorization cmtf for enhanced microbiome metabolome multi omic analysis |
| topic | biomarker identification latent factor dimension reduction functional pathway analysis multi-omics analysis |
| url | https://www.mdpi.com/2218-1989/15/1/51 |
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